Over the centuries, many wise mentors have expressed the idea, "To live is to learn." What technology has taught us recently, however, is that "Learning doesn't necessarily require living." Machine learning has become one of the hottest fields of research on the road to artificial intelligence. Many thousands of businesses have already applied machine learning to speed up business processes, improve sales closing rates and find patterns in raw data that more accurately predict future events. Data can hold secrets, especially if you have lots of it.

The biggest problem with year-end reviews is that, at least in marketing technology, a year can be a lifetime. So, to give you a head start on digesting this year of turmoil, here's a half-time review of this year's biggest trends. As of midsummer, I see four big buckets of trends-in-progress: Intelligence, Transparency, The Experience and Going Beyond Computers. Not only is every tool and platform getting way smarter, it's almost at the point where this new level of smartness isn't news anymore. Almost every self-respecting vendor is adding artificial intelligence (AI) to its resume these days, largely built around machine learning's pattern recognition and predictive analytics' projection of those patterns into the future.

Before a classification algorithm can do its magic, we need to train it by showing thousands of cat and non-cat images. The general principle in machine learning algorithms is to treat feature vectors as points in higher dimensional space. Then it tries to find planes or surfaces (contours) that separate higher dimensional space in a way that all examples from a particular class are on one side of the plane or surface. To build a predictive model we need neural networks. The neural network is a system of hardware and software similar to our brain to estimate functions that depend on the huge amount of unknown inputs.

I'm a software engineer and I've taken up a new hobby of Archery. On the side I've been experimenting with some basic classifiers in scikit-learn. A project I've gotten interested in is something to convert photos of archery targets post-shots into XY coordinate systems. As a first step my goal is just to tell if an image is an archery target at all. Doing some research it seems like TF image recognition might be an approach to take.

There was a time when every neighbourhood bank in North America and Europe was acquired by or merged with a larger institution. By 2000, global mega-banks offered fewer choices to consumers looking for competitive interest rates and other services. But the too big to fail banks are now facing competition in the form of a resurgence of customer-friendly, local banks. There is an even bigger challenge: Technology companies have been applying for financial licences that would allow them to enter the digital payments space. As traditional banks grapple with the challenges posed by fintech, legacy constraints and traditional operational models, artificial intelligence (AI) is emerging as the saviour.

In the past couple of weeks there has been a reinvigorated fervor surrounding artificial intelligence, with "AIO" (Artificial Intelligence Optimization) rearing its head on agency websites and blogs. HTTPS and mobile first seem to be cooling as topics, so attention is turning to RankBrain. The reality of this however is that artificial intelligence optimization is seemingly a paradoxical notion. If we imagine that Google is a child, when the child goes to school and reads a book, we want the child to learn and understand the information in that book. If the book isn't "optimized" for the child to learn – structured information, images, engaging, positive user experience etc. – then the child won't learn or understand the content.

Tasked with squeezing billions of transistors onto fingernail-sized slabs of silicon, chip designers are asking whether machine learning can help. In the view of electronic design automation firms, machine learning tools could chisel rough edges off complex chips, improving productivity, optimizing trade-offs like power consumption and timing, and testing that chips are ready for manufacturing. Though chip design is still a creative process, engineers need tools that abstract the massive number of variables in modern chips. Using statistics, the software generates models fitted to simulations that replicate how physical chips will work. The tools would seem to be prime candidates for machine learning, which can be trained to find hidden insights in data without explicit programming.

Now Facebook is ready, the company announced on Wednesday, to take a first and significant step in building a safer and more supportive Facebook community by significantly strengthening its own suicide prevention tools (Facebook has had suicide reporting and tools for a decade). Facebook is also testing a pattern recognition system, that will identify posts that include suicidal thoughts. In addition and perhaps in acknowledgement of the Live.Me video tragedy, Facebook is also introducing suicide prevention tools to Facebook Live posts. The updated system will also offer the option to connect directly with someone from several suicide prevention organizations including Crisis Text Line and the National Suicide Prevention Line.

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust \emph{time series cluster kernel} (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.

Adobe is pursuing a different tack: dishing out $50,000 no-strings-attached grants to professors and doctoral students working on projects of joint interest. "What academia provides is more the advanced mathematical algorithms and the advanced research that's gone into other related areas but hasn't been applied to our field," said Anil Kamath, Adobe's VP of technology. Adobe was not an early promoter of AI products as were other major technology players, like Google with its Automated Insights pattern-recognition tool, IBM with Watson and Einstein from Salesforce. But Adobe's research grant program, which has dished out 40 grants for a total of $2 million in the past four years, is bringing algorithmic AI into the company through academic work. Adobe is also doing outreach at events.